Elementary Statistics / Edition 2

Elementary Statistics / Edition 2

by Neil A. Weiss
     
 

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ISBN-10: 0201566400

ISBN-13: 9780201566406

Pub. Date: 12/28/1992

Publisher: Addison-Wesley

Now in its Fourth Edition, Weiss has a reputation for being thorough and precise, and for using real data extensively throughout. Case studies are introduced at the beginning of the chapters and discussed at the end, showing students the links between the subject matter and how the material can be used in real life.

Overview

Now in its Fourth Edition, Weiss has a reputation for being thorough and precise, and for using real data extensively throughout. Case studies are introduced at the beginning of the chapters and discussed at the end, showing students the links between the subject matter and how the material can be used in real life.

Product Details

ISBN-13:
9780201566406
Publisher:
Addison-Wesley
Publication date:
12/28/1992
Edition description:
Older Edition
Pages:
650
Product dimensions:
8.27(w) x 9.45(h) x (d)

Related Subjects

Table of Contents

I. INTRODUCTION.

1. The Nature of Statistics.
Case Study: Top Films of All Time.
Two Kinds of Statistics.
The Technology Center.
Simple Random Sampling.
Other Sampling Designs.
Experimental Designs.

II. DESCRIPTIVE STATISTICS.

2. Organizing Data.
Case Study: Preventing Infant Mortality.
Variables and Data.
Grouping Data.
Graphs and Charts.
Stem-and-Leaf Diagrams.
Distribution Shapes; Symmetry and Skewness.
Misleading Graphs.

3. Descriptive Measures.
Case Study: New York Yankees Y2K Salaries.
Measures of Center.
The Sample Mean.
Measures of Variation; the Sample Standard Deviation.
The Five-Number Summary; Boxplots.
Descriptive Measures for Populations; Use of Samples.

4. Descriptive Methods in Regression and Correlation.
Case Study: Fat Consumption and Prostate Cancer.
Linear Equations With One Independent Variable.
The Regression Equation.
The Coefficient of Determination.
Linear Correlation.

III. PROBABILITY, RANDOM VARIABLES, AND SAMPLING DISTRIBUTIONS.

5. Probability and Random Variables.
Case Study: The Powerball.
Probability Basics.
Events.
Some Rules of Probability.
*Discrete Random Variables and Probability Distributions.
*The Mean and Standard Deviation of a Discrete Random Variable.
*The Binomial Distribution.

6. The Normal Distribution.
Case Study: Chest Sizes of Scottish Militiamen.
Introducing Normally Distributed Variables.
Areas Under the Standard Normal Curve.
Working With Normally Distributed Variables.
AssessingNormality; Normal Probability Plots.

7. The Sampling Distribution of the Sample Mean.
Case Study: The Chesapeake and Ohio Freight Study.
Sampling Error; the Need for Sampling Distributions.
The Mean and Standard Deviation of x.
The Sampling Distribution of the Sample Mean.

IV. INFERENTIAL STATISTICS.

8. Confidence Intervals for One Population Mean.
Case Study: The Chips Ahoy! 1,000 Chips Challenge.
Estimating a Population Mean.
Confidence Intervals for One Population Mean When …sIs Known.
Margin of Error.
Confidence Intervals for One Population Mean When …sIs Unknown.

9. Hypothesis Tests for One Population Mean.
Case Study: Sex and Sense of Direction.
The Nature of Hypothesis Testing.
Terms, Errors, and Hypotheses.
Hypothesis Tests for One Population Mean When …s Is Known.
P-Values.
Hypothesis Tests for One Population Mean When …s is Unknown.

10. Inferences for Two Population Means.
Case Study: Breast Milk and IQ.
The Sampling Distribution of the Difference Between TwoSample Means for Independent Samples.
Inferences for Two Population Means Using Independent Samples:Standard Deviations Assumed Equal.
Inferences for Two Population Means Using Independent Samples:Standard Deviations Not Assumed Equal.
Inferences for Two Population Means Using Paired Samples.

11. Inferences for Population Proportions.
Case Study: Double-Dipping ATM Fees.
Confidence Intervals for One Population Proportion.
Hypothesis Tests for One Population Proportion.
Inferences for Two Population Proportions Using IndependentSamples.

12. Chi-Square Procedures.
Case Study: Road Rage.
The Chi-Square Distribution.
Chi-Square Goodness-Of-Fit Test.
Contingency Tables; Association.
Chi-Square Independence Test.

13. Analysis of Variance (ANOVA).
Case Study: Heavy Drinking Among College Students.
The F-Distribution.
One-Way ANOVA: The Logic.
One-Way ANOVA: The Procedure.

14. Inferential Methods In Regression and Correlation.
Case Study: Fat Consumption and Prostate Cancer.
The Regression Model; Analysis of Residuals.
Inferences for the Slope of the Population Regression Line.
Estimation and Prediction.
Inferences in Correlation.

APPENDIXES.

Appendix A. Statistical Tables.
Appendix B. Answers To Selected Exercises.
Index.

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